EV Charging Field Energy Prediction for Load Balancing
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Solution Overview
Problem
The increasing demand for electric vehicle charging poses challenges in managing energy consumption and predicting charging requests, which can lead to power supply risks and environmental impacts.
Innovation Solution
A management system and method that utilizes a server to receive and analyze charging data from electric vehicle charging stations, generating energy prediction data to estimate energy consumption and manage charging requests, including load balancing and demand response procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of electric vehicle charging stations is increased to meet growing charging demand, then charging service capacity is improved, but power consumption and power supply risk increase
Solution Approach 1:
The system performs preliminary actions by predicting energy consumption before charging operations occur. The server uses historical charging data to generate energy prediction data for future time periods, allowing charging fields to plan power supply in advance and avoid power supply risks before they occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting actual charging data and comparing it with predicted energy consumption. The server receives charging data from charging stations, updates prediction models based on actual versus predicted values, and adjusts future energy predictions to improve accuracy and power supply planning.
2Productivity
If load balancing operations are implemented to increase charging station capacity without updating power equipment, then charging station quantity is improved, but power output per station must be reduced
Solution Approach 1:
The system applies dynamics by enabling flexible adjustment of power output for individual charging stations based on real-time conditions. The server can dynamically reduce power output of specific stations during peak periods while maintaining overall charging capacity, allowing the charging field to accommodate more vehicles without requiring infrastructure upgrades.
3Reliability
If demand response procedures are implemented during peak power consumption, then power supply stability is improved, but charging service flexibility is reduced
Solution Approach 1:
The system performs preliminary action by predicting energy consumption peaks before they occur. The server generates energy prediction data that identifies future high-consumption periods, allowing the charging field to proactively implement demand response measures and inform users in advance, rather than imposing restrictions reactively during peak periods.
4Reliability
If energy prediction is implemented to plan power consumption, then power supply-consumption balance is improved, but system complexity increases
Solution Approach 1:
The server performs multiple functions using a single integrated system. It collects charging data from multiple stations, predicts energy consumption for future periods, compares predictions with actual values, and generates management recommendations. This multi-functional approach achieves power supply-consumption balance without requiring separate complex systems for each function.
Data Source
AI summary
Management methods and systems for energy and charging requests of an electric vehicle charging field are provided. First charging data corresponding to at least one first charging operation is received by a server from each of electric vehicle charging stations in a charging field via a network during a first predetermined period, wherein the charging data includes at least a charging start time, a charging period, and an output power. According to the first charging data corresponding to the at least one first charging operation received from each of the electric vehicle charging stations during the first predetermined period, the server generates an energy prediction data of the charging field in a second predetermined period, wherein the energy prediction data includes at least an energy consumption estimation of the charging field at a specific time point.